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Substructure-based Neural Machine Translation for Retrosynthetic Prediction
ChemRxiv Pub Date : 2020-07-31 , DOI: 10.26434/chemrxiv.12740801.v2
Umit Ucak 1 , Taek Kang , Junsu Ko , Juyong Lee
Affiliation  

This work presents a new template-free neural machine translation method for retrosynthetic reaction prediction by learning the chemical change at a substructural level. The proposed method effectively solves all the translation issues arising from SMILES-based representation of molecular structures.



中文翻译:

基于子结构的神经机器翻译用于合成预测

这项工作提出了一种新的无模板神经机器翻译方法,用于通过学习子结构水平的化学变化来预测逆合成反应。所提出的方法有效地解决了由基于SMILES的分子结构表示所引起的所有翻译问题。

更新日期:2020-07-31
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